{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Mn72Q0WNHcfyvjmLzCt0Wb+nHbfz2+9fx7388Ry6XY6tdN+NHk05gw02Hr/a9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\nMSrsIiI1RoVdRKTG/D8LOPKllSefvAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb64f22e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 创建40个点\n",
    "x_data = np.r_[np.random.randn(20, 2) - [2, 2], np.random.randn(20, 2) + [2, 2]]\n",
    "y_data = [0]*20 +[1]*20\n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit the model\n",
    "model = svm.SVC(kernel='linear')\n",
    "model.fit(x_data, y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.45680901, 0.46005048]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04135744])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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49NFH2bBhA//5z3/4/fffqVKlChMnTnT5ScWU2R/l0w7lUUNK3QBS7EI4mFKK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\nxUixCyGEi/l/fAVtQwvgrPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb65a8438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 获取分离平面 \n",
    "\n",
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "x_test = np.array([[-5],[5]])\n",
    "d = -model.intercept_/model.coef_[0][1]\n",
    "k = -model.coef_[0][0]/model.coef_[0][1]\n",
    "y_test = d + k*x_test\n",
    "plt.plot(x_test, y_test, 'k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.03493567, -1.23548968],\n",
       "       [ 2.7351459 , -0.63297521],\n",
       "       [ 0.17575617,  1.90969795]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.support_vectors_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 画出通过支持向量的分界线\n",
    "b1 = model.support_vectors_[0]\n",
    "y_down = k*x_test + (b1[1] - k*b1[0])\n",
    "b2 = model.support_vectors_[-1]\n",
    "y_up = k*x_test + (b2[1] - k*b2[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pfp2UFoj+V9nTOpdCmJTVg4w+/KC+/Kba5LReR1XbPANTDkxZ/3oh3dJ6E2JC\nwMlXV63YCb15mlKJzY3mz69W8GfOQIMGylnyhx/AYVIT4mG+/dChQ3z00UdMnz6dAgUK0LRpU/5J\nKDeRRvHyUhMcT55UfW+ZM8c1NV+7Bpef4jkmhBnhUhbhWvlhUAcQzsUxpRuCcK8N1ls8N6gDmLO+\nsG5hyoBwKamDegpEB3aj8PGBPn3UyL5YZ0l/f2VhAA7jDQ88zLcHBwfTv39/Nm3aRIUKFahYsaLD\nOUs2bary7bVrq2OjRqmN1g4d1NzWF0FGbQPCEvFMd4RnlxeVq0nB6MBuNM7O0Lw57Nmj8vDp0ql0\nTYUKqtP1hOOYHuXIkYNvv/2WkJAQxo8fz8WLF2nSpAmFChVi8uTJhIUlJkilfoSI+2LXoQO0b68m\nOxUrpjJ2mzcn8kSmHCS8oWpGuUN6qpu3P8Kthk20a1IGOrCnFIRQ9fCgmpxKlFCz2woXVoZjDuQs\n6eXlRffu3Tl58iS//vormTNnplu3bvj6+vLll19y+Wm5iTRIgQIwebJqiRg8WM2EmTYt7vFnfZkR\nHh8ATxrVCWVoluUvVU6ZdQcmz1bJIV1jJIkZjGrrmyHDrFMjFy9KOWiQlBkzqkHcy5cbrcgQrFar\n/Pvvv2WjRo2kEEK6uLjI9u3byyNHjhgtze6EhUl56ZK6f+iQlH5+UgYGSnn7dsLPt0Zsl5bLb0nL\npZLScrGEtFypK63RQXbTq7EtJHKYtQ7sqYF796ScPl3KyEj186xZUo4fL+Xdu4bKMoITJ07ITz/9\nVLq5uUlAvvfee3LTpk3SarUaLc3u7N0rZZUq6l3s4yNlQICUISHxn2e1WqQ16ri0RgfbXaPGtujA\nnpb58EP1T5chg5T9+0t54YLRiuzOlStX5ODBg2WWLFkkIEuVKiXnz58vo6KijJZmd3bvlrJZMynN\nZinTp5cyPNxoRZrkIrGBXefYUyMLF8L27VC9OowYoWa0jhljtCq7kiVLFr766iuCg4OZPn06YWFh\ntGzZkvz58zNmzBjuONC82jJlYMECNSZg5kxlMiYldOumOl0dZGtG8wg6sKdW3noLFi9WVTOdO8dZ\nBV+4oExJHOTd7O7uTseOHTly5AgrV64kX758+Pv74+vry+eff05oaKjREu1GnjzQuLG6f+ECLFsG\ndeuqP41ZsxymRUKDDuypn/z51bCPdx7M05w2Td1//XWYO9fhnCU3b97M7t27qVu3LmPHjiVfvnx8\n/PHH7Evs1Is0Qs6cqot1zhzV7NSunQr8Bw4YrUxjD2wS2IUQtYUQx4UQp4QQ/WxxTs1LMmCAKpOM\niYFWrZSzpIOlacqUKcOCBQteUljNAAAgAElEQVQ4ffo03bt3Z/ny5ZQqVcrhnCVdXOCTT5R7xR9/\nKBvhQoXUY5s3q944TRolMYn4Z91Q3Q6ngXwoy7kDQNFnvUZvntoBq1XK1aulrFFD7azFcvmycZoM\n4ubNm3LEiBEyZ86cEpDFihWTM2fOlBEREUZLMwSrVcpChaQUQsomTaT85x+jFWkSC3bcPC0HnJJS\nnpFq6OIvQEMbnFeTFISAOnVUvn32bHXs8GH1HT2209VBSJ8+PX379uXMmTPMmTMHs9lM+/btyZMn\nD8OGDePGjeSZC5pSEQI2bYJ+/WDjRrVdU7EibNlitDKNrbBFYM8JPLpDde7BMU1KweWBd3fmzNCr\nF/z+O5Qtq76br1rlML40Li4ufPLJJ+zfv58//viDkiVLMnDgQHx9fenevTunHSg38corathXaKga\n/nXxYtwcmDt3VPOzJvVii8Ce0CiXeElMIUQnIcQeIcSeq1ev2uCyaQMpJdfOX+f+HTu8k7JnV65S\n587FOUs2awa3byf/tVMQQghq1qzJ2rVrOXjwIB9++CHTpk2jYMGCNG3alB07dhgt0W7EOkueOAH1\n6qljI0cq47Evv3y6s6QmZWOLwH4OeHRScS7gwpNPklJOl1KWkVKWyZIliw0um/rZvW4/LXN/SusC\n3WmatT1fNRrBvVtPDiJOBh51lvzrL8iQQZVH1q2rhnBfu5b8GlIIJUqUYNasWZw9e5YvvviCjRs3\n8tZbb1GpUiV+++03h3GWdHKKGwdQr56a7vjtt6pFomNHOPqMMaealIctAvtuoIAQIq8QwgVoBqyw\nwXnTNEGHQxjcZBRXz10nKiKamKgYdq/dz5cNvrOfCGdnKF1a3b97VyVfv/pKLde6dlUG4Q7CK6+8\nwrBhwwgNDWXcuHGcP3+exo0bU7hwYaZMmeIwzpIA5cvDb7+pAV9t26rRvYMGGa1K8yIkObBLKWOA\nz4B1wFFgkZTySFLPm9ZZOnYV0ZExjx2LiYrh5N4zhB4/b39BPj4q937kCLRooVoYCxWCdevsr8VA\nvLy86NGjBydPnmTRokVkzJiRrl274ufnx1dffeVQzpIFC8KUKcpZMjBQHTt+XG3PLFgA0dHG6tM8\nHZvUsUspV0spC0op80spv7XFOdM6505dwmqJv2np5OLE5WADUyFFi6qZrMHByie2ShV1fMkS1enq\nMKkJJz744AN27NjB1q1bqVSpEkOHDiV37tx07NiRow6Um8iSRTU3AVy5or7ctWgBr76qWiQcyL0h\n1aA7Tw2i5NtFcHaNPwQhKjKa/CVzG6DoCbJnV7tnbm7q56lT4YMP1DJuwgQ1qNMBEEJQqVIlli1b\nxrFjx2jbti3z5s2jaNGiDztdpYM0PIHKvf/3H6xYoYK9v7/6YveoXYGMPoj1Rmusl8tivdYQGbHB\nML2Oig7sBtGwe108vN0wmeP+CVw9XKnboQYZsqU3UNlTWLtWrdqzZ4cePVQe/scfjVZlVwoWLMiU\nKVMICQlh8ODB7Nq1i2rVqlG2bFkWLFhAtIPkJkwmNWj7r79g1y61yerqqvbfv/7yMvv+GgJR/4C8\nDTFHkbf6YA1bYrRsh0IYsdooU6aM3ONADTJP40roNeZ8vZDda/fjld6Txr3eo27HmgiRUAVpCmL7\ndpV0bdNGvcOvXlXf0YsVM1qZXQkPD2fu3LmMGTOG48eP4+fnR8+ePenQoQM+Pj5Gy7M7wcFQrFgE\n9++7UePtbfTp/CO1qm1VY/5EBkTW7QhhNlpmqkYIsVdKWea5T0xMe6qtb9pSII3x5ZfKH752bSk3\nbFA96w6ExWKRK1askFWqVJGA9PHxkZ9//rkMDQ01WprduXasqhw+cKR8JfslCVIWL3xMHtlSS1ou\nFpNWy3Wj5aV60H7sGrvRsycMHarcpmrWhFKlVNmEg2Aymahfvz6bN29m165d1KlTh8DAQPLmzfuw\n09VRyJDJi76f/cDpndWZNa4vmTPexC/nBcDMv/u8cTD3BsPQgV2TdDJlgoED4exZVSYZFaUqaGIJ\nDzdMmr0pW7Ysv/zyC6dPn+azzz7jt99+44033njY6SrT+Ear8PoMcMPFJZpWHy5j45JWeHiAdG9B\n8+bO+PqqLZozZ4xWmrbRgV1jO9zclPH3oUMqwIMqfM6eXXW6Bgcbq8+O5MmTh7Fjx3Lu3DlGjBjB\n0aNHqVOnDq+99hqzZs0iMo1OvRButcBnIIj0gCvgDh4tMfkEsHixKqyaOhUKFICmTbU/fLKRmHyN\nrW86x+5AnDolZcuWaiCn2Sxl8+ZS7tljtCq7ExkZKWfPni1fe+01Ccjs2bPLb7/9Vl6/njbzzlZr\njLTGXJVWa2S8x86fl7JfPzWfdfFidSw8XMqYGDuLTIWQyBy7rorR2IeQEGUjOH26GgJy4QKkjyvr\nlFJyaOtRdqzai2c6d6q3qEyOvNkMFJw8SCnZsGEDo0ePZv369Xh4eNC+fXt69epFvnz5jJZnV+7d\nA3d3MJvhf/9TA7/69IHWrcHDw2h1KZPEVsXowJ4MBB0O4UrwVfK/nofMOTMZLSdlcfu2Kn5+5x1V\n+Ny6NdZKlRi58TbbVh8g4n4kTi5mTGYz/jO6UL15ZaMVJxsHDx5kzJgx/Pzzz1gsFho3bkxAQABv\nvvmm0dLszqpVyn9u1y61ZdO1qxrGnS3tfbYnCR3YDeDOjbsMem84Zw6F4ORsJioimndaV6Hn5I6Y\nTKlvO+POjbusnLKOfRsPkyNvVhr3eo+8JWzYFXvzJtSoAfv2cVO4sVzmYyX5uSNcAXB1d2HRpRl4\neLvb7popkAsXLjBhwgSmTp3KrVu3qFixIgEBAdSvXx+z2XHqvqWEbdtg9GjV2dqoESxdarSqlEVi\nA3vqizYpmFFtJnFyXxCRYZHcvx1GdGQ0G+dtZeXU9UZLe2FuXLpJx+J9+PnbpRzYfIT1c/6i+1sD\n2LFqr+0ukiED7N3LvGpdOSHT04b/mM9qSkjl1292NrNv4yHbXS+F8sorrzB8+PDHnCXff/99ihQp\n4lDOkkJApUqwbJlylvz2gevU6dOqD27zZhX8Nc9HB3Ybcf9OGHvWHyAm6nHHxsiwSJaNX22IplP7\ngvj7t51cDHpxR8L5Q5dw+/pdoiJUm7zVYiUyLIoxHadgteXEJSG44FeEQaIS7XmXdeThBBkAeCP6\nAulPHbbdtVI4jzpLLly4kPTp0z90lvz666+5cuWK0RLtRsGCUKSIun/iBOzcCdWqKWfJX35R2zSa\np6MDu40IvxeByZSwFcD92/Zdcd25cZdu5frR++0vGdV2Eh2K9Wb4x+NfaGjEzt//xRId//nhdyO4\neMa21rXvtq6Km6crIcKHieINIoUTAM0jD1Ds8/ZqKOeSJQ7lLPnhhx+yc+dOtmzZQsWKFRkyZAh+\nfn506tSJY8eOGS3RrtSpoyplp01TzpLNmyv3Cgex5nkpdGC3EZlyZCB9tnTxjpudTLxZt5RdtYxu\nN5kzB84ScT+SsDvhREVEs23ZTpZ+/3uiz+GVwTPB4xaLBc90ti1ZeL1acRp0rYWLmwsubs64ebni\n5ulK+LJVMHGi8qFp2lQt4xwo6SqEoHLlyixfvpyjR4/Spk0b5s6dS5EiRahfvz5//fVXmm94isXd\nHTp1UpOcVqxQ950fmKNOmqSmPWri0JunNuTfDQf5utFIoiKjsVqsuLg54+HtzpR/R9qtOib8XjiN\nM7eLlxICyJ4nC3PPTE7Uef6Y8xfju/1AxP24Rhqzs5mSVYoxYv2XNtP7KOdOXmTPuv14eLtTsVFZ\nPNM9+HCxWGD5cjWv1d9fBflbt1RHa44cyaIlpXL16lUmT57MxIkTuXbtGqVLlyYgIICmTZvi5ORk\ntDy7c/q0+rw3mdRK3t8fSpY0WlXyoatiXpBbV2+zfvZmLp6+TPFKRajctDwuCfilP4/Q4+f5bdxq\nQo9foGTVojToWhufTN7JoDhhbl+7Q7NcnRMM7Okye7P4SuKsdqWUTAuYw4op63BxdcISbSV3sVx8\n+/sA0mU20LlQSrXL9r//qd21jz9Wxc8O6iwZGBjIiRMnyJ0790NnSW9v+/29pQSCgmDcODUf5v59\nZVc0fTrkzWu0MtujA/sLcHzPafrWGExMjIWo8CjcvNzI/EpGJuwYhlf6hFMSKRUpJW0L9eD8qUuP\nHTeZTdRoWZm+P332Que7efkWp/YFkTlXJvIW97Ol1KRx6hSMHQuzZqmVe506EBAA1asbrcyuWK1W\nVq1aRWBgIFu2bCFdunR06tSJHj16kCtXLqPl2ZWbN1VAnzsXduwALy81ttfPT/nFpwV0YE8kUkra\nFenFuRMXHjvu7OpEg2616TK6dZKvcfHMZfZtOoxXeg/efK8Uru7J+1d2+O+j9K/zLTFRFmKiY3Bx\nd1Epob0j0l7D1PXrajDnhAlQrhysXKmOW63q+7kDsWvXLgIDA1m8eDEmk4nmzZvj7+9PybScm0iA\n2C91VisUL66ydj16QOfOqsI2NWOXwC6EGAXUB6KA00BbKeWt570uJQX2axdu0OrVz4iOiL/FnsU3\nEz8HT33pc6t0xmxWTlmPMAnMZhPCbGLEui8pVPbVpMh+LheDLrN80lrOHbtA8cpFqNuxBj4Z0/BX\n9IgIuHEDXnlFJV5r1IDPPoOOHSFd/E3ttExQUBDjxo1jxowZ3L9/n5o1axIQEMC7776b8oe42BAp\n4Y8/1EyY9evB0xPat4feveNmuKY27BXY3wU2SSljhBAjAKSUXzzvdSkpsN++dofmuToTnUBO+pX8\n2Zh9cuJLn3vn6n8Z+tGYxzYgAdJnTccv56e9dFdhVEQUa37cxOZftuHu7Ub9LrUoX6/0M9+0t67e\nZsnYVexZd4DMOTPS1L8+Jauk0bz0wYPKI37zZvD2VsG9Z0/1ndyBuHnzJtOnT2fcuHFcvHiR4sWL\n4+/vT/PmzXFNK7mJRHLggBq8vWABzJ6tNlotFuVTk5qwS+eplHK9lDI2Iu4AUl1SL11mH14tne+x\n2aOg2tnrdqyZpHOv/mFDvKAOEBUexdF/TrzUOaOjoulT5St+6DuPw38fY/ea/Qxr8T0z+s176mtu\nXrlNp9f8WTJ2Faf2BbFj1V4GvjecNTM3vpSGFM9rr8Gff8KePVCvntpZK1pUFUE7EBkyZOCLL77g\n7Nmz/PTTTwghaNu2LXnz5mX48OHcvHnTaIl2o2RJFdCDglRRFcDIkWo497Jlaa9FwpZJyHbAGhue\nz24M/LkXmXNmxN3bHVd3F1w9XHmtajEa93ovSeeNDI9K+AHBw47OF2Xr4h0EHz1HZFjcB0bE/UiW\nTVjDldBrCb5m4chl3L15n+jIuG8lkWGRTO0zm6jINNzlUbo0/Pyzmurw449q9Q6qiub331US1gFw\ncXGhdevWHDhwgHXr1lGiRAkGDBiAr68vPXr04IwDTb3ImTOu/j1HDggNhfffV12uU6ZAWnFveG5g\nF0JsEEIcTuDW8JHnDARigPnPOE8nIcQeIcSeq1ev2ka9jciWOwtzTk1k4IJedBnThrFb/sew3wfg\n7PLi5Y6PUqNFZdw843/llVZJsYqFXuqcO1f/S8S9+N8CzE5mDm05muBrdq/dn2D5I0DIUQfo7PDz\ngw8/VPevXYNff1Ur+eLFVY1cRISx+uyEEIJ3332XdevWceDAAZo0acLUqVMpUKDAw05XR6JNG1Vc\ntXChcpDu2lUdSws8N7BLKWtKKYsncFsOIIRoDdQDWspnJOyllNOllGWklGWyZMliu9/ARpidzLxZ\ntxT1Or9DgVK28cWu3qISRd4qiJuXGwBOzmZc3F3wn9n1pStjMmRLj9kp/j+bEAKfzAlvjmZMoCMW\nICY6Jsk16VGR0Rzc8h///XP8hSwLDCNzZrWCnzdP1cB17Ai5cyu/WAfitddeY/bs2QQFBfH555+z\nfv16ypcv/7DT1aZ+QCkYJyf1mb9zJ2zZAv36qePBwaq7NbW6NyR187Q2MAaoIqVM9DI8JW2eJjcW\ni4U9a/ezY9VefDJ7U6tNNV7Jn/2lzxdy7DxdS/eNl+bJkC0dC0KnYXaKvxuU0Cauk7OZohUKEfjn\n4JfWsmPVXoZ/PA5QFUBuHq4MWdEv2St+bIaUKhc/ZYqqh/fyUr6x2bLBq6nkd7ARd+/e5ccff2Ts\n2LEEBwdToEAB+vTpQ6tWrfBwwKkXixfDJ5+oL3P16qkWibffVmWURmKvqphTqMGG1x8c2iGl7PK8\n1zlSYE8O/lq0ncCOUxBCYLVK0mXy5tvf+5O7qO9TX/Nr4Apmf7UQs7OZmGgLBUvn45uln7/0iv1K\nyFXaFe1FZNjjHzCe6Tz45fx03DwS943kSshVZvSfz561+3HzcqNB19p84F8/wQ8ou/DGG6qEolEj\n9W6uUMEYHQYRExPD0qVLGT16NLt37yZTpkx069aNbt26kTVrVqPl2ZWrV2HyZGVXdO2a8qLbskWt\n8o1CNyilcaIioji++zRunq68+kbeRNUnh98LJ+hQCBmypSdHvqSNppnSexbLJq7Fann8K7u7txt9\npneh6kcVn3uOO9fv0q5IL+7evPfwPK4eLlRoWI4B83s+fN65Exf4+7ddmEyCyk3KJ1n7M7l0Sb2T\nJ09WrYxvvQVDhzpcR6uUkq1btxIYGMiKFStwdXWldevW9OnTh0KFXm5/KLUSHg5z5qiN1qFD1bFF\ni1Szs73dG3Rg1yQbS8etYlrA3HhBHVSZaKdRrWjQtdZzz/PzsCXMH7okXoWQi5szM46MJUfebA+e\nsxSLxYIQApNJ0HHkJzT6rI7Nfp8EuX9fpWfGjlXv5ubN1TFQnS4OxLFjxxg7diyzZ88mMjKS+vXr\n4+/vz9tvv+1QDU+xHD8OhQurvrfOnVVXa86c9rm2nqDkoNy+dofLwVeTzc710tkrzOz/c4JBHUAC\nr1cvnqhzHf77WIJln04uTgQdDCH46Dnmf7uUqIgoLNEWYqJiiIqI5oe+c7kSksyVVZ6eqnP1xIm4\nipoJE1SFzaBBamXvIBQuXJhp06YREhLC119/zT///EPVqlUpV64cCxcuJMbBpl4UKqQ2W2vVUmP8\n8uSBVq3g4kWjlcWhA3sa4calmwRU/4bmvp1pX7QXLXN/yr5Nth8rt3357qeOJ3NyduKdVm/jV/jZ\ny5cTe08zc8B87t64l2Au3RJjJXverGyct4WoiIR7AbYt2/3C2l8KszmuPbF6dahSBYYNU5U07dvD\nkSP20ZECyJo1K9988w0hISFMnTqV27dv06xZM1599VW+//577jpQA1i5cqpM8tQpVSa5YQPE7jFf\nvmz8CD8d2NMAUkr6vvM/Dv99jOjIGCLDo7h67jpfNRxh82lHQoiEKwMEvP3BW/Sc3OmZr/+h3zz6\nVPmKhSOXc2LvGSwxj5dIOrk4ke+13OQp7qs6Y5/yBjEkBVCunBr0cfw4dOig+tP9/e2vw2Dc3d3p\n3Lkzx44dY/ny5fj5+dG7d298fX354osvOH/+vNES7UbevKqxOThYpWakhHfeUZ2uP/0EkfFbTuyC\nDuxpgKM7T3Il+Fq8IBkTFcPyyWtteq2KjcomeNzF1ZlPvv7gmQH31P4glk9cQ2RYFNIqH6ZzhFCT\nppxcnHirQRmGrR7Agc1HCL+XcOOQJcZKhafosAsFCqixPSEhaqMV1P1y5VSnq4PMbDOZTDRo0IAt\nW7awc+dOatWqxejRo8mTJw+tW7fm4MGDRku0G7HdrFar+qyXEtq2VYH/u+/UPrw90YE9DXA19Doi\ngXmrMdEWLjzhy57UxpOsflnoHNgaFzdnnF2dcXZxwsXNmTZDmpGrwLOnGW1ftusxW4NYnF2daTOk\nOctu/sRXi/zxSu/JueMXnvp1tkj5AmT1zZyk38MmZM4cV+9+4YLyomnZEvLnV5aCd+4Yq8+OxObb\nT506RdeuXVmyZAklS5Z82OnqKCP8zGZo3Vr50K1dq5qb+/dX4/zsiQ7saYBCZfMnaBng6uHCG9WL\nI6Vk8diVNM3ajlpOH9G6YHe2r3j5HHWDT2sx69g4OgxvSfvhLZlxeCwf+Dd47uucXJwS/AASQuDp\n4/5YN27uYr6YzfH/PF3dXaj58dsvrT3ZKF9e5dtXrVKBPSAA8uWLq6RxEPLmzcu4ceMIDQ1l+PDh\nHD58mNq1a1OyZElmz55NVNRT/JPSGEKozdX162H/flVUBaoe3h7owJ4GyJ4nK1WbVcT1kaYgJ2cz\nPpm8qdW2Oj8PW8pPXy7k9jW1uXXh1CWGNf+evX8ceOlrZvXLQuNe79Gkd71E15W//UGFBDdLpYSK\n75d77FiJykXIVegVnF3jukFMZhOe6Tyo3qLSS+tOVkwmeO+9OGfJIUPiSiOHD4d//zVWnx3JkCED\n/fr1e+gsKaWkTZs25M2bl++++87hnCVdXNR9Ly/7XFPXsacRrFYrv0/7g+WT1hJ2N5yKjcrRclAT\nvNJ70iRzO8Luhsd7TeFyrzJhx3BA+bUf2PwfHt5uvF69eJIN0J7G8klrmP75XIRJIBBYrVZ6/9CF\nmi3jr8Lv3wnjh75z2fTz38REW3izbim6jmtLllypbArU5csqL3/3rqqs8feH2rUdasKTlJL169cT\nGBjIH3/8gaenJ+3bt6dXr17kTYvDSZMJ3aCkAZQXe8s8nyY4Icorgye/Xf+JxWNW8uPABTi5mBEI\nnFzMDFsziEJl8ieLpmvnr7Nj1b+YzCbealCGDFkdYMLRrVvwww+qhOL8eeUT+8svyjvewThw4ABj\nxozh559/xmq10qRJEwICAihXrtzzX+zg6MCuAcASY6FJlnbcvx3faLpohUJ0Ht2KvjUHx/N88cnk\nzcIL03FyNtAYIy0SFaX60adPV/NZ06VTO205c0KmVPZNJImcO3eOCRMmMG3aNG7fvk3lypUJCAig\nXr16mBzo28yLoDtPNYCyI24xsMlj+XdQm5DthjZnzYwNRIXHX83HRMVwYLPjNN/YDRcX+Phj5SYV\nW/jcqhX4+qpO19OnjVZoN3LlysWIESMIDQ196CrZsGFDihQpwrRp0wgPj58+1CQOHdgdgA/869Ml\nsBWZc2bE7GQmT3FfvvmtLyWrFuP+nfCES9EET60j19gQIZQ3fLNmKlVToAA0aeJQG63e3t706tWL\n06dP88svv+Dj40OXLl3w8/Pjm2++4cqVK0ZLTHXoVIyDs3nhNgI7TIk3m9XFzZkF56bhk9HO9nWO\nzMWLquFpyhRlPta6tWpddHJKfVOXk0Css+To0aNZuXIlbm5utGrVyiGdJZ9Ep2I0iaJyk/IULlfg\n4Qg/k0ng6u5Cx5Gf6KBub3LkgG+/Vf6wsYXPEycq16lJkxymJl4Iwdtvv82KFSs4evQorVq1Yvbs\n2RQuXJiGDRuyZcsWh2l4eln0il2DJcbC37/tYuvSHXin96ROhxoULJ08FTGaF2TtWvjmG2UnmDEj\nfPqpysVnf/kpXKmRK1euMGnSJCZNmsT169cpW7YsAQEBNG7cGCcjJ1/YGV0Vo9GkFaSE7duVTcGy\nZfDuuyrgOyBhYWHMmTOHMWPGcPLkSXLnzk3v3r1p164d3vaeemEAOhWj0aQVhICKFeOcJUeOVMfP\nn4cGDVSnq4OkJjw8POjSpQvHjh1j2bJl+Pr60qtXL3x9fenXr59DOUs+Cx3YNZrURIECcU1Nx47B\njh2qm7VMGYdzlmzYsCFbt25lx44dvPvuu4waNYq8efM6nLNkQtgksAshAoQQUgiRAiz3NBoHoUYN\nZRf8ww8QFqacJQsVggjHKlN98803WbRoESdPnqRLly4PnSVr1arF+vXrHXKjNcmBXQjhC7wDhCRd\njkajeSHc3NTQjyNHVCdrhw7qGKiAHxpqrD47ki9fPsaPH09ISAjDhg3j0KFD1KpVy+GcJcE2K/ax\nQF+eOutGo9EkOyYT1KsHAwaon8+fVzPb8uVTna779hmrz45kzJiR/v37ExQUxKxZsx5zlhwxYgS3\nbt0yWmKyk6TALoRoAJyXUj7X/1UI0UkIsUcIsefq1WQeRKzRJBGLxcLScb/TplB3PsrZie+7TOfG\npVRkNZszpxrI2b07LF8OpUqp1M3Jk0Yrsxuurq60adOGgwcPsnbtWooWLUq/fv0ebriePXvWaInJ\nxnPLHYUQG4CEimYHAgOAd6WUt4UQZ4EyUsrnWsnrckdNSmdE6wlsXbKTyDDVkWt2MpM+qw8zj4zF\nM52nwepekFu3lOnYzJnwzz+qHj4oCF55BVxdn//6NMT+/fsZM2YMCxYswGq10rRpUwICAihb1sBR\niy+AzcodpZQ1pZTFn7wBZ4C8wIEHQT0X8K8QwrE6JzRpjotBl9ny6z8PgzqoJq57N++zZuYmA5W9\nJOnTQ9++qoomY0ZVGvnBB5AnDwwbBjduGK3Qbrz++uvMmTOHoKAgAgICWLt2LeXKlaNKlSqsWLEi\nyaMjUwovnYqRUh6SUmaVUuaRUuYBzgGlpJSXnvNSTSok/F44185fTzN/+M/i5N4zOLnE72aMDI/i\nwF+p2PHy0UHj332nRvsMHBjnLHnmjHHa7MyjzpJjxozh7NmzD50lp0+fnuqdJXUdu+aZhN+PYPjH\n42iSpT2tC/agWc7ObF2yw2hZyUq23FmwWuJ/gDk5O5Gr4CsGKLIxQkDNmqp79eBB+PBDlarZ9ODb\niAN8eMfi4+ND7969OX36NAsWLMDb25vOnTuTO3duBg8eTGrdD7RZYH+wcrfTqFaNvfju4/H8vXQn\n0ZHRRIVHcfPyLUa0nsB//xw3WlqyUbBMfl7Jnx0n58cdFZ1czDToWssgVclEiRIwaxYEB6vqGVDG\nY7GdrhaLsfrshJOTE82aNWP37t1s3ryZN998k2+++QY/Pz+6dOnC8eOp6+9dr9g1T+XGpZvsXruf\nqCfG6kWFR/HLd8sMUpX8CCEY8ceXvF69OE4uTji7OZMjX1aGrupPjryJG9yd6siRI67+PXNmZSHc\npIlqeJo8WTVAOQBCCGcGtq0AAAm5SURBVKpUqcLKlSv577//+OSTT/jpp58oUqTIw07X1NDwpE3A\nNE/l+J7T9K05mLA78fONuYvmYsbhsQaosi/3bt0nIiySTDkyIB7NUad1LBb47TcYPVo5S773Hqxa\nZbQqQ7h8+TKTJ09+6CxZrlw5/P39DXGW1CZgmiTjW+gVLNHxv4qbncwUr1TEAEX2xyu9J5lfyehY\nQR3UYI+mTVV55N9/w6BB6vjly9CpE/z3n7H67Ei2bNkYPHgwISEhTJ48mRs3bvDRRx9RoEABxo8f\nz71794yWGA8d2DVPxcPbnQ/7Nnw4hAPUIA43T1ea9WtkoDKN3Yh1lixfXv28axfMnQvFiqlOVwdz\nlvz00085duwYv/32Gzlz5qRnz574+vrSv39/Lly4YLTEh+hUjOaZSCnZOH8rC0cu49aVO5SsWoy2\nQ5uR89UcRkvTGMXVq2p838SJ6n7ZsrB1q8M1OwHs2LGDwMBAli5ditlspkWLFvj7+1OiRIlkuZ4e\ntKHRaJKX8HA1iPv4cZWLB5WXr1EDfHyM1WZnzpw5w/fff8/MmTMJCwujVq1a+Pv7U7NmTZum8XRg\n12g09iU4GPLmBW9v6NwZevSAXLmMVmVXbty4wdSpU5kwYQKXLl3itddew9/fn2bNmuHi4pLk8+vN\nU41GY19y51YVNHXqwJgxKsh/8olDWQdnzJiRAQMGcPbsWX788UcsFgutW7e2u7OkDuwajcZ2lC0L\nv/yinCU/+wzWrYurj7961WE2Wl1dXWnbti2HDh1izZo1FClS5KGz5Fo7zKvVgV2j0diePHlg7Fg4\ndw6yZFHHGjZUY/1mzYLIyGe+PK0ghKB27dps2LCBffv20aRJE0qVKpXs19WBXaPRJB+xeWUp4dNP\n1UCQdu0c1lnyp59+ImvWrMl+LR3YNRpN8iOEyrfv3w/r16uV+8CBKm2jsTk6sGs0GvshBLzzjsq9\nHzgArVur4z/8oDzid6Rt51B7oQO7RqMxhtdeA88H06giImDDBnjrLahUSdXDO4izZHKgA7tGozGe\n7t1VWeT48XDhAjRuDC1aGK0q1aIDu0ajSRl4eakAf+IELFqkmpwArl2Dr79WBmSaRKEDu0ajSVk4\nOal8e/Xq6ucNG2DIENUA1bEjHD1qrL5UgA7sGo0mZdOsmRrE3bat8qYpWhTq14fo6Oe/1kFJcmAX\nQnQXQhwXQhwRQoy0hSiNRqN5jIIFlaNkSAh8841qenJ2Vo9t3QoxMYbKS2kkafyHEKIa0BB4TUoZ\nKYRI/sp7jUbjuGTJovLtsQQFQZUq4OsLvXpBhw7KhMzBSeqK/VPgOyllJICU8krSJWk0Gk0iyZ0b\nli9Xnax9+ig3yb591YarA5PUwF4QqCyE2CmE+EsIUdYWojQajSZRmEwq3/7XX2q6U506MGlSXGom\nPP68XkfguakYIcQGIHsCDw188PoMQHmgLLBICJFPJmDyLoToBHQC8PPzS4pmjUajiU+ss+TNm5Ah\ngzpWu7byq/H3h1q1VOerA/DcFbuUsqaUsngCt+XAOWCpVOwCrEDmp5xnupSyjJSyTJZYtzeNRqOx\nNbFB3WqFunXV4O06dVSn608/OYSzZFJTMcuA6gBCiIKAC+DYyS2NRpMyMJngiy/UBuvs2Wq13rYt\nzJxptLJkJ6mB/UcgnxDiMPAL0DqhNIxGo9EYhosLtGqlTMfWrVMukwDz56vxfWfOGKsvGUhSYJdS\nRkkpP36QmiklpdxkK2EajUZjU4SAd9+NK4c8eRKmToUCBeDDD9VYvzSC7jzVaDSOyTffqDTN558r\nj/jy5eP8aVI5OrBrNBrHJWdO+O475Sz5/fdQr546fvOm6nQNCzNW30uiA7tGo9F4e0PPnqomHmDp\nUujaFfz84KuvUp2zpA7sGo1G8yTt2sGWLWrox9ChqsO1U6dU40mjA7tGo9E8iRBQuTIsWxbnLHnl\nirIUBmUdnIILAHVg12g0mmcR6yz522/q5+BgKFEirtM1Ba7idWDXaDSaxBBrR5A1K0yeDHf/394d\nvGhVxWEc/z6OSUJFgkHgDNOAm6TCQERwUWQTVsO0EyaKgbYFCklU/gmB06Igsk2QEEETQRBl0Lao\nTINhapCYGq3ISElXofNrcV5pkEl7xznnMOc+n83Mve8L9zm88HDee9977gWYmICtW2FqCi5erJtv\nCRe7mVk/Nm5M59tnZ9PKksPDaUXJ8+fT64uLdfPhYjczW5l162B8PK0sOTeXlgyG9JPJK3e61opW\n7chmZq0YGUl/L19O5+Snp2H7dhgdTcsYFL7Q6mI3M1stAwPpRqeFhXTj08xMWjr4yJGiMVzsZmar\nbdOmtLLk/HxaKnjfvrT/3Lkih7+hZ56amdk1bNgAk5P/bq8vU7mesZuZlVLoQdsudjOzxrjYzcwa\n42I3M2uMi93MrDEudjOzxrjYzcwa42I3M2uMi93MrDGKCk8BkXQW+Kn4gW/cZuCP2iEK6tp4wWPu\nirU65uGIuON6b6pS7GuVpK8jYkftHKV0bbzgMXdF62P2qRgzs8a42M3MGuNi78+btQMU1rXxgsfc\nFU2P2efYzcwa4xm7mVljXOwrIOmgpJC0uXaW3CS9Iul7Sd9J+kDS7bUz5SJpr6QfJJ2S9GLtPLlJ\nGpL0uaRZSTOS9tfOVIKkAUnfSvqodpZcXOx9kjQEjAI/185SyDHgnoi4D5gDXqqcJwtJA8DrwKPA\nNmBC0ra6qbK7BDwfEXcDu4BnOzBmgP3AbO0QObnY+zcFvAB04uJERHwaEZd6m18AgzXzZLQTOBUR\nP0bE38C7wBOVM2UVEb9GxPHe/xdIZbelbqq8JA0CjwNv1c6Sk4u9D5LGgTMRcbJ2lkqeAT6uHSKT\nLcDCku3TNF5yS0m6C7gf+LJukuxeJU3MFmsHyckPs76KpM+AO5d56RDwMvBI2UT5XWvMEfFh7z2H\nSF/dj5bMVpCW2deJb2WSbgHeBw5ExF+18+QiaQz4PSK+kfRg7Tw5udivEhEPL7df0r3ACHBSEqRT\nEscl7YyI3wpGXHX/NeYrJE0CY8CeaPf3saeBoSXbg8AvlbIUI+kmUqkfjYjp2nky2w2MS3oMuBm4\nTdI7EfFU5Vyrzr9jXyFJ88COiFiLCwn9b5L2AoeBByLibO08uUhaT7o4vAc4A3wFPBkRM1WDZaQ0\nQ3kb+DMiDtTOU1Jvxn4wIsZqZ8nB59jtel4DbgWOSToh6Y3agXLoXSB+DviEdBHxvZZLvWc38DTw\nUO+zPdGbzdoa5xm7mVljPGM3M2uMi93MrDEudjOzxrjYzcwa42I3M2uMi93MrDEudjOzxrjYzcwa\n8w8FcNuZuLrtYwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1afb65dbb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x_data[:,0],x_data[:,1],c=y_data)\n",
    "x_test = np.array([[-5],[5]])\n",
    "d = -model.intercept_/model.coef_[0][1]\n",
    "k = -model.coef_[0][0]/model.coef_[0][1]\n",
    "y_test = d + k*x_test\n",
    "plt.plot(x_test, y_test, 'k')\n",
    "plt.plot(x_test, y_down, 'r--')\n",
    "plt.plot(x_test, y_up, 'b--')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
